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Consumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.
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The article deals with the research on assessment preferences reflected in learning styles within English for Specific Purposes (ESP) instruction on the higher education level. The sample group consisted of 287 respondents of the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. The main objective of the research was to discover expected correlations between respondents’ learning styles and relating preferences in selected assessment formats. Two questionnaires were applied to reach the objective; however, the expectations did not prove. The discovered findings were discussed within the world context.